Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Proceedings of the conference on Visualization '01
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Neural Networks for Volumetric MR Imaging of the Brain
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
RGVis: Region Growing Based Techniques for Volume Visualization
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
IEEE Transactions on Visualization and Computer Graphics
A Novel Interface for Higher-Dimensional Classification of Volume Data
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A variational approach to automatic segmentation of RNFL on OCT data sets of the retina
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hi-index | 0.00 |
We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have “global awareness.” A feature, such as a retinal layer, can therefore be modeled